REGRESSION

Question 1

statistical technique for finding the best-fitting straight line for
a set of data

Answer 1

regression

Question 2

the best-fitting straight line for
a set of data or resulting straight line is
called

Answer 2

regression line

Question 3

Y=bX+a

Answer 3

Linear Equation

Question 4

Y=bX+a

Answer 4

Regression Equation

Question 5

Y=bX+a what is the slope

Answer 5

b

Question 6

determines how much the Y variable changes when X is
increased by one point.

Answer 6

slope (b)

Question 7

Y=bX+a The value of a in the general equation is called

Answer 7

Y-intercept

Question 8

it determines the value of Y when X = 0

Answer 8

a or the Y-intercept

(Y=bX+a)

Question 9

On a graph, the _ value identifies the point where the line intercepts the Y-axis

Answer 9

a (Y=bX+a)

Question 10

means that Y increases when X is increased, what slope

Answer 10

positive slope

Question 11

indicates that Y decreases when X is increased, what slope

Answer 11

negative slope

Question 12

regression equation for Y is the _ equation

Answer 12

linear equation

Question 13

distance between the actual data point (Y) and the predicted point on the line (Ŷ) is defined as

formula:

Answer 13

Y – Ŷ

Question 14

The Regression Equation for Prediction

Ŷ =

Answer 14

Ŷ = bX + a

Question 15

The goal of _ is to find the equation for the line that minimizes these (Y – Ŷ) distances.

Answer 15

regression

Question 16

gives a measure of the standard distance between the predicted Y values on the regression line and the actual Y values in the data.

Answer 16

standard error of estimate

Question 17

process of testing the significance of a regression equation and is very similar to the analysis of variance (ANOVA)

analysis of

Answer 17

analysis of regression

Question 18

The variability for the original Y scores (both SS and df) is partitioned into _
components

Answer 18

TWO

Question 19

(1) the variability that is predicted by the regression
equation and
(2) the residual variability

Answer 19

two components of variability for the original Y scores

Question 20

(1) the variability that is predicted by the regression equation

Answer 20

components of variability for the original Y scores

Question 21

(2) the residual variability

Answer 21

components of variability for the original Y scores

Question 22

The slope of the regression equation (b or beta) is zero. what hypotheses in analysis of regression?

Answer 22

Ho

Question 23

The slope of the regression equation (b or beta) is not zero. what hypotheses in analysis of regression?

Answer 23

H1

Question 24

Variable X significantly predicts variable Y. what hypotheses in analysis of regression?

Answer 24

H1

Question 25

process of using several predictor variables to help obtain more accurate predictions.

Answer 25

Multiple Regression

Question 26

different _ variables are **related to each other**, which means that they are often **measuring and predicting the same thing**.

Answer 26

**predictor variables**

Question 27

Is adding more variables to the equation always better?

Answer 27

no

Question 28

the variables may _ with each other, **adding another predictor variable to a regression equation does not always add to the accuracy of prediction.**

Answer 28

variables may overlap with each other

Question 29

Regression Equations with Two Predictors

Answer 29

Multiple Regression

Question 30

Ŷ = b₁X₁ + b₂x₂ + a

Answer 30

Regression Equations with Two Predictors/Multiple Regression

Question 31

describes the proportion of the total variability of the Y scores that is accounted for by the regression equation.

Answer 31

R²

Question 32

can be defined as the standard distance between the predicted Y values (from the regression equation) and the actual Y values (in the data)

Answer 32

standard error of estimate

Question 33

use to determine whether the equation predicts a significant portion of the variance for the Y scores.

Answer 33

F-ratio

Question 34

determines the value of Y when X = 0

Answer 34

**a** = Constant or the Y-intercept

Question 35

_ analysis evaluates the contribution of each predictor variable after the influence of the other predictor has been considered.

Answer 35

**regression analysis** (Partial Correlations (β))

Question 36

you can determine whether each predictor variable contributes to the relationship by itself or simply duplicates the contribution already made by another variable.

Answer 36

Partial Correlations (β) (regression analysis)

Question 37

R², F value (F), degrees of freedom (numerator, denominator; in parentheses separated by a comma next to F), and significance level (p), β. Report the β and the corresponding t-test for that predictors for each predictor in the regression. (R²=.358, F(2,55)=5.56, p<.01). (β = .56, p<.001), as did agreeableness ((β= -.36, p<.01).

Answer 37

Reporting Results Regression

Question 38

_ table presents the analysis of regression evaluating the significance of the regression equation, including the F-ratio and the level of significance (the p value or alpha level for the test).

Answer 38

ANOVA TABLE

Question 39

summarizes the unstandardized and the standardized coefficients for the regression equation. _ table

Answer 39

Coefficients table

Question 40

The standardized coefficients are the _ values

Answer 40

beta (b) values.

Question 41

For one predictor, beta is simply the _ correlation between X and Y.

Answer 41

Pearson Correlation

Question 42

the table uses a _ statistic to **evaluate the significance of each predictor variable**. For **one predictor variable,** this is **identical to the significance of the regression equation** and you should find that **t is equal to the square root of the F-ratio** from the analysis of regression.

Answer 42

t statistic

Question 43

For two predictor variables, the t values measure the _ of the contribution of each variable beyond what is already predicted by the other variable.

Answer 43

significance

Question 44

On a graph, it identifies the point where the line intercepts the Y-axis

Answer 44

Y-intercept/Constant

Question 45

is the actual data point

Answer 45

Y

Question 46

the predicted point on the line (the straight line)

Answer 46

Ŷ (Y hat)

Question 47

each data point is an _ of X and Y

Answer 47

intersection

Question 48

actual data point may be _ from the computed regression equation

Answer 48

different

Question 49

perfect correlation

Answer 49

correlation is r = +1.00